The goal of most empirical studies in social sciences and medical research is to determine whether an alteration in an intervention or a treatment will cause a change in the desired outcome response. Unlike randomized designs, establishing the causal relationship based on observational studies is a challenging problem because the ceteris paribus condition is violated. When the covariates of interest are measured with errors, evaluating the causal effects becomes a thorny issue. We propose a semiparametric method to establish the causal relationship, which yields a consistent estimator of the average causal effect. The method we proposed results in locally efficient estimators of the covariate effects. We study their theoretical properties and demonstrate their finite sample performance on simulated data. We further apply the proposed method to the Stroke Recovery in Underserved Populations (SRUP) study by the National Institute on Aging.

中文翻译：

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在存在易错协变量的情况下评估因果效应的半参数方法

大多数社会科学和医学研究的实证研究的目标是确定干预或治疗的改变是否会导致预期结果反应的变化。与随机设计不同，基于观察性研究建立因果关系是一个具有挑战性的问题，因为违反了其他条件不变。当感兴趣的协变量测量有误差时，评估因果效应就成为一个棘手的问题。我们提出了一种半参数方法来建立因果关系，它产生了平均因果效应的一致估计量。我们提出的方法导致协变量效应的局部有效估计量。我们研究了它们的理论特性并证明了它们在模拟数据上的有限样本性能。